Pricing Strategies for Different Accuracy Models from the Same Dataset Based on Generalized Hotelling's Law

• URL: http://arxiv.org/abs/2404.05272v2
• Date: Sat, 29 Mar 2025 08:49:42 GMT
• Title: Pricing Strategies for Different Accuracy Models from the Same Dataset Based on Generalized Hotelling's Law
• Authors: Jie Liu, Tao Feng, Yan Jiang, Peizheng Wang, Chao Wu,
• Abstract summary: We consider a scenario where a seller possesses a dataset $D$ and trains it into models of varying accuracies for sale in the market.<n>The dataset can be reused to train models with different accuracies, and the training cost is independent of the sales volume.
• Score: 9.353146025394372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider a scenario where a seller possesses a dataset $D$ and trains it into models of varying accuracies for sale in the market. Due to the reproducibility of data, the dataset can be reused to train models with different accuracies, and the training cost is independent of the sales volume. These two characteristics lead to fundamental differences between the data trading market and traditional trading markets. The introduction of different models into the market inevitably gives rise to competition. However, due to the varying accuracies of these models, traditional multi-oligopoly games are not applicable. We consider a generalized Hotelling's law, where the accuracy of the models is abstracted as distance. Buyers choose to purchase models based on a trade-off between accuracy and price, while sellers determine their pricing strategies based on the market's demand. We present two pricing strategies: static pricing strategy and dynamic pricing strategy, and we focus on the static pricing strategy. We propose static pricing mechanisms based on various market conditions and provide an example. Finally, we demonstrate that our pricing strategy remains robust in the context of incomplete information games.

Related papers

• Markets for Models [0.0]
  We study markets in which firms sell models to a consumer to help improve their prediction. We show that market structure can depend on subtle and nonmonotonic ways on the statistical properties of available models.
  arXiv  Detail & Related papers  (2025-03-04T19:07:02Z)
• Why is the estimation of metaorder impact with public market data so challenging? [0.6990493129893112]
  Using models of price and trade based on public market data provide average price trajectories which are qualitatively different from what is observed during real metaorder executions. We propose a modified Transient Impact Model which provides more realistic trajectories by assuming that only a fraction of the metaorder trading triggers market order flow.
  arXiv  Detail & Related papers  (2025-01-28T17:29:08Z)
• STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading [55.02735046724146]
  In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. We propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM. Storm extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings.
  arXiv  Detail & Related papers  (2024-12-12T17:15:49Z)
• A Survey on Data Markets [73.07800441775814]
  Growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place. It serves as a coordinating mechanism by which several functions, including the pricing and the distribution of data, interact.
  arXiv  Detail & Related papers  (2024-11-09T15:09:24Z)
• Tabular Data Synthesis with Differential Privacy: A Survey [24.500349285858597]
  Data sharing is a prerequisite for collaborative innovation, enabling organizations to leverage diverse datasets for deeper insights. Data synthesis tackles this by generating artificial datasets that preserve the statistical characteristics of real data. Differentially private data synthesis has emerged as a promising approach to privacy-aware data sharing.
  arXiv  Detail & Related papers  (2024-11-04T06:32:48Z)
• DAVED: Data Acquisition via Experimental Design for Data Markets [25.300193837833426]
  We propose a federated approach to the data acquisition problem that is inspired by linear experimental design. Our proposed data acquisition method achieves lower prediction error without requiring labeled validation data. The key insight of our work is that a method that directly estimates the benefit of acquiring data for test set prediction is particularly compatible with a decentralized market setting.
  arXiv  Detail & Related papers  (2024-03-20T18:05:52Z)
• An Auction-based Marketplace for Model Trading in Federated Learning [54.79736037670377]
  Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. We frame FL as a marketplace of models, where clients act as both buyers and sellers. We propose an auction-based solution to ensure proper pricing based on performance gain.
  arXiv  Detail & Related papers  (2024-02-02T07:25:53Z)
• Natural Language Processing and Multimodal Stock Price Prediction [0.8702432681310401]
  This paper utilizes stock percentage change as training data, in contrast to the traditional use of raw currency values. The choice of percentage change aims to provide models with context regarding the significance of price fluctuations. The study employs specialized BERT natural language processing models to predict stock price trends.
  arXiv  Detail & Related papers  (2024-01-03T01:21:30Z)
• Privacy-Aware Data Acquisition under Data Similarity in Regression Markets [29.64195175524365]
  We show that data similarity and privacy preferences are integral to market design. We numerically evaluate how data similarity affects market participation and traded data value.
  arXiv  Detail & Related papers  (2023-12-05T09:39:04Z)
• Data Acquisition: A New Frontier in Data-centric AI [65.90972015426274]
  We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets. We then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers. Our evaluation of the submitted strategies underlines the need for effective data acquisition strategies in Machine Learning.
  arXiv  Detail & Related papers  (2023-11-22T22:15:17Z)
• SWDPM: A Social Welfare-Optimized Data Pricing Mechanism [21.487641773601737]
  We propose a novel approach to modeling multi-round data trading with progressively disclosed information. We introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54% in trading feasibility.
  arXiv  Detail & Related papers  (2023-05-08T02:25:35Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• A Survey of Data Pricing for Data Marketplaces [77.3189288320768]
  This paper attempts to comprehensively review the state-of-the-art on existing data pricing studies. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices.
  arXiv  Detail & Related papers  (2023-03-07T04:35:56Z)
• Learning to simulate realistic limit order book markets from data as a World Agent [1.1470070927586016]
  Multi-agent market simulators usually require careful calibration to emulate real markets. Poorly calibrated simulators can lead to misleading conclusions. We propose a world model simulator that accurately emulates a limit order book market.
  arXiv  Detail & Related papers  (2022-09-26T09:17:11Z)
• Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows [62.997667081978825]
  In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts.
  arXiv  Detail & Related papers  (2022-05-27T08:38:20Z)
• Lessons from the AdKDD'21 Privacy-Preserving ML Challenge [57.365745458033075]
  A prominent proposal at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD'21. A key finding is that learning models on large, aggregated data in the presence of a small set of unaggregated data points can be surprisingly efficient and cheap.
  arXiv  Detail & Related papers  (2022-01-31T11:09:59Z)
• Multi-Asset Spot and Option Market Simulation [52.77024349608834]
  We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows. We leverage the conditional invertibility property of normalizing flows and introduce a scalable method to calibrate the joint distribution of a set of independent simulators.
  arXiv  Detail & Related papers  (2021-12-13T17:34:28Z)
• Data Sharing Markets [95.13209326119153]
  We study a setup where each agent can be both buyer and seller of data. We consider two cases: bilateral data exchange (trading data with data) and unilateral data exchange (trading data with money)
  arXiv  Detail & Related papers  (2021-07-19T06:00:34Z)
• Causally Constrained Data Synthesis for Private Data Release [36.80484740314504]
  Using synthetic data which reflects certain statistical properties of the original data preserves the privacy of the original data. Prior works utilize differentially private data release mechanisms to provide formal privacy guarantees. We propose incorporating causal information into the training process to favorably modify the aforementioned trade-off.
  arXiv  Detail & Related papers  (2021-05-27T13:46:57Z)
• OSOUM Framework for Trading Data Research [79.0383470835073]
  We supply, to the best of our knowledge, the first open source simulation platform, Open SOUrce Market Simulator (OSOUM) to analyze trading markets and specifically data markets. We describe and implement a specific data market model, consisting of two types of agents: sellers who own various datasets available for acquisition, and buyers searching for relevant and beneficial datasets for purchase. Although commercial frameworks, intended for handling data markets, already exist, we provide a free and extensive end-to-end research tool for simulating possible behavior for both buyers and sellers participating in (data) markets.
  arXiv  Detail & Related papers  (2021-02-18T09:20:26Z)
• Beating the market with a bad predictive model [0.0]
  We prove that it is generally possible to make systematic profits with a completely inferior price-predicting model. The key idea is to alter the training objective of the predictive models to explicitly decorrelate them from the market.
  arXiv  Detail & Related papers  (2020-10-23T16:20:35Z)
• Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications [110.54266632357673]
  We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
  arXiv  Detail & Related papers  (2020-05-09T01:32:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.